Despite the improvement in survival for patients with liver cancer (LCa) in recent decades, only one in five patients survive for 5 years after diagnosis. Thus, there is an urgent need to find new treatment options to improve patient survival. For various cancers, including LCa, the chemokine CCL5 (RANTES) facilitates tumor progression and metastasis. Since the function of the CCR5/CCL5 interaction in LCa cell proliferation and migration is poorly understood, the present study was undertaken to investigate the role of the CCR5/CCL5 axis in these processes. Flow cytometry, RT-PCR, Western blot, and immunofluorescence techniques were used to quantify the expression of CCR5 and CCL5 in LCa cells. To determine the biological significance of CCR5 expressed by LCa cell lines, a tissue microarray of LCas stained for CCR5 and CCL5 was analyzed. The results showed higher expression (p < 0.001) of CCR5 and CCL5 in hepatocellular carcinoma (HCC) tissues compared to non-neoplastic liver tissues. Furthermore, to delineate the role of the CCR5/CCL5 interaction in LCa cell proliferation and migration, various LCa cells were treated with maraviroc, a CCR5 antagonist, in the presence of CCL5. These data demonstrated the biological and clinical significance of the CCR5/CCL5 axis in LCa progression. The targeting of this axis is a promising avenue for the treatment of LCa.

Inter-institutional collaborations and partnerships play fundamental roles in developing and diversifying the basic biomedical, behavioral, and clinical research enterprise at resource-limited, minority-serving institutions. In conjunction with the Research Centers in Minority Institutions (RCMI) Program National Conference in Bethesda, Maryland, in December 2019, a special workshop was convened to summarize current practices and to explore future strategies to strengthen and sustain inter-institutional collaborations and partnerships with research-intensive majority-serving institutions. Representative examples of current inter-institutional collaborations at RCMI grantee institutions are presented. Practical approaches used to leverage institutional resources through collaborations and partnerships within regional and national network programs are summarized. Challenges and opportunities related to such collaborations are provided.

Background: Social determinants of health (SDOH) are critical contributors to cancer disparities, influencing prevention, early detection, treatment access, and survival outcomes. Addressing these disparities is essential in achieving equitable oncology care. Artificial intelligence (AI) is revolutionizing oncology by leveraging advanced computational methods to address SDOH-driven disparities through predictive analytics, data integration, and precision medicine. Methods: This review synthesizes findings from systematic reviews and original research on AI applications in cancer-focused SDOH research. Key methodologies include machine learning (ML), natural language processing (NLP), deep learning-based medical imaging, and explainable AI (XAI). Special emphasis is placed on AI’s ability to analyze large-scale oncology datasets, including electronic health records (EHRs), geographic information systems (GIS), and real-world clinical trial data, to enhance cancer risk stratification, optimize screening programs, and improve resource allocation. Results: AI has demonstrated significant advancements in cancer diagnostics, treatment planning, and survival prediction by integrating SDOH data. AI-driven radiomics and histopathology have enhanced early detection, particularly in underserved populations. Predictive modeling has improved personalized oncology care, enabling stratification based on socioeconomic and environmental factors. However, challenges remain, including AI bias in screening, trial underrepresentation, and treatment recommendation disparities. Conclusions: AI holds substantial potential to reduce cancer disparities by integrating SDOH into risk prediction, screening, and treatment personalization. Ethical deployment, bias mitigation, and robust regulatory frameworks are essential in ensuring fairness in AI-driven oncology. Integrating AI into precision oncology and public health strategies can bridge cancer care gaps, enhance early detection, and improve treatment outcomes for vulnerable populations.

The Research Centers in Minority Institutions, (RCMI) Program was established by Congress to address the health research and training needs of minority populations, by preparing future generations of scientists at these institutions, with a track record of producing minority scholars in medicine, science, and technology. The RCMI Consortium consists of the RCMI Specialized Centers and a Coordinating Center (CC). The RCMI-CC leverages the scientific expertise, technologies, and innovations of RCMI Centers to accelerate the delivery of solutions to address health disparities in communities that are most impacted. There is increasing recognition that the gap in representation of racial/ethnic groups and women is perpetuated by institutional cultures lacking inclusion and equity. The objective of this work is to provide a framework for inclusive excellence by developing a systematic evaluation process with common data elements that can track the inter-linked goals of workforce diversity and health equity. At its core, the RCMI Program embodies the trinity of diversity, equity, and inclusion. We propose a realist evaluation framework and a logic model that integrates the institutional context to develop common data metrics for inclusive excellence. The RCMI-CC will collaborate with NIH-funded institutions and research consortia to disseminate and scale this model.